Our research is now published in #Nature 𝗕𝗶𝗼𝗺𝗲𝗱𝗶𝗰𝗮𝗹 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴! 🤩🤯🤩 And it's like the stars have aligned perfectly: this publication is coming out on... my birthday! 🎂 Best gift I could ask for! 😍😍 @halperineran @UCLA @CompMedUCLA @UCLAHealth @natBME

📄 Paper: https://t.co/QM2695bRVc 🧪 Try it:

📢 Just published! M1CR0B1AL1Z3R🦠 v2.0 is now live and published in Nucleic Acids Research 🧬 A powerful, user-friendly web server for large-scale #microbial #genome #analysis. Proud to be co-senior author on this follow-up to my PhD work 🚀 @NAR_Open

📣📣📣 Thrilled to speak at @CUAnschutz about potential avenues to tackle pressing challenges in research and care using #AI. I’ll be for a few days so if you’re in the area, hit me up!

Ready to ramp up? Check out our user-friendly GitHub repo:

For detailed info, here's a free-access version of our paper in case you missed it:

For a more layman-friendly overview, check out my LinkedIn post:

UCLA just unveiled SLIViT, an AI model that analyzes 3D medical images faster and cheaper than human experts. Key points: 1️⃣ Analyzes MRIs, CTs, and more in a fraction of the time. 2️⃣ Detects disease markers across multiple scan types 3️⃣ Outperforms other models, making

@nvidia @NVIDIAAIDev @NVIDIAAI

Researchers @halperineran and @orenavram at #UCLA have developed a new, #AI-powered foundation model that can accurately analyze #3D medical imagery, like MRIs and CT scans, in a fraction of the time it would otherwise take a human expert. ➡️ https://t.co/iwc7PoqGTB The model

@UCLA @CompMedUCLA @UCLAengineering @DohenyEye @UCLATDG @UCLAHealth @halperineran Link to our @natBME paper https://t.co/q57S6Bwdni

@UCLA @CompMedUCLA @UCLAengineering @DohenyEye @UCLATDG @UCLAHealth @halperineran

Early and accurate detection can dramatically improve patient care. Just imagine, for example, the impact this could have for individuals in remote areas where retina specialists may not be available, but OCT machines are!

Now, with SLIViT and just a few hundred (!) annotated scans, we achieve expert-level detection in milliseconds! 🚀

In real-life practice, this process involves multiple rounds and many 'back-and-forth's, and typically takes muuuuuch longer (5-10 minutes per volume), as these risk factors are often challenging to identify.

To give a sense of the task complexity our model manages, here’s an illustration of the "analysis procedure" (fast-forwarded!) performed by retina specialists on a 97-frame OCT volume (retinal scan) to detect subtle risk factors associated with blindness-causing diseases.

The article explores how our cutting-edge #AI model, designed for fast and cost-efficient analysis, is revolutionizing the field of 3D medical imaging and diagnostics 🏥💻

✨ NVIDIA ✨ has featured #SLIViT today in their blog post! 🤯🤯🤯 https://t.co/1X2xvKUb29

Researchers at UCLA have developed a new deep-learning AI framework called SLIViT that can quickly analyze complex medical scans including MRIs and 3D medical images which consistently performs at a level comparable to clinical specialists across various types of medical imaging.

SLIViT -New AI model efficiently reaches clinical-expert-level accuracy in complex medical scans. @natBME By @CompMedUCLA @DohenyEye @UCLAengineering @UCLAHealth @halperineran , SriniVas R. Sadda, MD, @orenavram https://t.co/5AcZZdLHoF

An #AI model assessing CT, MRI, retinal OCT, cardiac echo volumetric images "as accurate as clinical specialists who had spent considerable time [almost four orders of magnitude faster] manually annotating the analysed scans" https://t.co/Oo17jYIlF3 @NatBME @orenavram